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A Location-Aware Adaptive Vehicle Dynamics System (LAAVDS) is developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. An integral part of this System is an Intervention Strategy that uses a novel measure of handling capability, the Performance Margin, to assess the need to intervene. Through this strategy, the driver's commands are modulated to affect desired changes to the Performance Margin in a manner that is minimally intrusive to the driver's control authority. Real-time implementation requires the development of computationally efficient predictive vehicle models. This work develops one means to alter the future vehicle states: modulating the driver's brake commands. This control strategy must be considered in relationship to changes in the throttle commands. Three key elements of this strategy are developed in this work.

A Location-Aware Adaptive Vehicle Dynamics System (LAAVDS) is developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. An Intervention Strategy uses a novel measure of handling capability, the Performance Margin, to assess the need to intervene. The driver's commands are modulated to affect desired changes to the Performance Margin in a manner that is minimally intrusive to the driver's control authority. Real-time implementation requires the development of computationally efficient predictive vehicle models which is the focus of this work. This work develops one means to alter the future vehicle states: modulating the driver's throttle commands. First, changes to the longitudinal force are translated to changes in engine torque based on the current operating state (torque and speed) of the engine.

During the last years mechatronic systems developed into one of the biggest drivers of innovation in the automotive industry. The start of production of systems like dual clutch transmission, lane departure warning systems and active suspensions proves this statement. These systems have an influence on the longitudinal, steering and vertical dynamics of the vehicle. That is why the interaction on vehicle level is crucial for an optimal result in the fields of efficiency, comfort, safety and dynamics. To optimize the interaction of mechatronic systems, in this paper a new test rig concept for a complete vehicle is presented. The so-called Car-in-the-Loop-concept is capable of realistically reproducing the loads, which act on the powertrain, the steering and the suspension during a test drive.

Driving cycles play a fundamental role in the design of components, in the optimization of control strategies for drivetrain topologies, and in the identification of vehicle properties. The focus on a single or a few test cycles results in a risk of non-optimal or even poor design regarding the real usage profiles. Ideally, multiple different driving cycles that are representative of the real and scattering operating conditions are used. Therefore, tools for the stochastic generation of representative driving cycles are required, and many works have addressed this issue with different approaches. Until now, the stochastic generation of representative testing cycles has been limited to low dimensionality, and only a few works have studied higher dimensionality using Markov chain theory. However, it is mandatory to create tools that can stochastically generate multidimensional cycles incorporating all relevant operating conditions and maintaining signal dependency at the same time.

A unique concept for a multi-body test rig enabling the simulation of longitudinal, steering and vertical dynamics was developed at the Institute for Mechatronic Systems (IMS) at TU Darmstadt. A prototype of this IMS test rig is currently being built. In conjunction with the IMS test rig, the Vehicle Terrain Performance Laboratory (VTPL) at Virginia Tech further developed a full car, seven degree of freedom (7 DOF) simulation model capable of accurately reproducing measured displacement, pitch, and roll of the vehicle body due to terrain excitation. The results of the 7 DOF car model were used as the reference input to the multi-body IMS test rig model. The goal of the IMS/VTPL joint effort was to determine whether or not a controller for the IMS test rig vertical actuator could accurately reproduce wheel displacements due to different measured terrain constraints.

One seminal question that faces a vehicle's driver (either human or computer) is predicting the capability of the vehicle as it encounters upcoming terrain. A Performance Margin (PM) is defined in this work as the ratio of the required tractive effort to the available tractive effort for the front and rear respectively. This simple definition stems from and incorporates many traditional handling metrics and is robust in its scope of applicability. The PM is implemented in an Intervention Strategy demonstrating its use to avoid situations in which the vehicle exceeds its handling capabilities. Results from a design case study are presented to show the potential efficacy of developing a PM-based control system.

One seminal question that faces a vehicle's driver (either human or computer) is predicting the capability of the vehicle as it encounters upcoming terrain. A Location-Aware Adaptive Vehicle Dynamics (LAAVD) System is developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. In contrast to current active safety systems, this system is predictive rather than reactive. This work provides the conceptual groundwork for the proposed system. The LAAVD System employs a predictor-corrector method in which the driver's input commands (throttle, brake, steering) and upcoming driving environment (terrain, traffic, weather) are predicted. An Intervention Strategy uses a novel measure of handling capability, the Performance Margin, to assess the need to intervene. The driver's throttle and brake control are modulated to affect desired changes to the Performance Margin in a manner that is minimally intrusive to the driver's control authority.

Mercedes-Benz BlueTEC passenger cars have been on the cutting edge of clean diesel technology since 2006. These BlueTEC vehicles furthermore passed millions of kilometers in the hands of customers. SCR-equipped passenger cars already meet the most stringent exhaust emissions standards in international markets such as the USA, Europe and Japan. Diesel engines with BlueTEC technology also reduce CO₂ emissions and provide the high torque and performance associated with the diesel engine in addition to keeping exhaust emissions at the lowest possible level. Nowadays the requirements for SCR emission concepts are increasing continuously. In fact the emission legislation is getting stricter with the LEVIII emission standards in 2015. Additionally the requirements and effort for on-board diagnosis are increasing year after year. In combination with ambitious CO₂ targets all these issues constitute the further challenges of BlueTEC SCR emission concepts for worldwide markets.

An online and real-time Condition Prediction system, so-called lifetime monitoring system, was developed at the Institute for Mechatronic Systems in Mechanical Engineering (IMS) of the TU Darmstadt, which is intended for implementation in standard control units of series production cars. Without additional hardware and only based on sensors and signals already available in a standard car, the lifetime monitoring system aims at recording the load/usage profiles of transmission components in aggregated form and at estimating continuously their remaining useful life. For this purpose, the dynamic transmission input and output torques are acquired realistically through sensor fusion. In a further step, the lifetime monitoring system is used as an input-module for the introduction of innovative procedures to more load appropriate dimensioning, cost-efficient lightweight design, failure-free operation and predictive maintenance of transmissions.

In order to offer a wide range of driving experiences to their customers, original equipment manufacturers implement different driving programs. The driver is capable of manually switching between these programs which alter drivability parameters in the engine control unit. As a result, acceleration forces and gradients are modified, changing the perceived driving experience. Nowadays, drivability is calibrated iteratively through road testing. Hence, the resulting set of parameters incorporated within the engine control unit is strongly dependent on the individual sentiments and decisions of the test engineers. It is shown, that implementing a set of objective criteria offers a way to reduce the influences of personal preferences and sentiments in the drivability calibration process. In combination with the expertise of the test engineers, the desired vehicle behavior can be formalized into a transient set point sequence to give final shape to the acceleration behavior.

The Institute for Mechatronic Systems in Mechanical Engineering (IMS) designed a concept for a test rig, which enables the simulation of longitudinal, steering and vertical dynamics for a complete vehicle under laboratory conditions. The main part of the test rig concept is a shaft, which contains three constant velocity joints and two ball-spline supported length compensations. It connects the wheel hub of the test car to an electric motor. In addition a linear actuator is mounted to the middle part of the shaft and a hydraulic actuator replaces the suspension strut. These actuators can load the longitudinal, steering and vertical degree of freedom of the test car according to simulated driving maneuvers. A prototype of this concept is being built at the IMS lab. Beginning with a precise explanation of the test rig concept this paper discusses the control strategy for the rotational speed of the wheel hub of the car mounted on the test rig based on a simulation.

Referring to the transmission development, three different classifications of the power train are useful. These are the conventional power train with combustion-engined drive of the wheels, the electric power train with electromotive drive of the wheels and the hybrid power train with both types of drive. Due to this division, the micro hybrid belongs to the conventional power train while the serial hybrid is classified with the electric power train. Subdivisions of the electric power train are the decentralized drives near the axle shafts or the wheel hub drive and the central drive with differential. The choice of the electric motor is dependent on different influences such as the package, the costs or the application area. Furthermore the execution of the transmission system does influence the electric motor. Wheel hub drives are usually executed on wheel speed level or with single ratio transmission.

The U.S. Army uses the root mean squared of elevation, or the RMSE standard for characterizing road/off-road roughness descriptions. This standard has often appeared in contracts as a performance requirement for the vehicle system. One important application of the standard is describing the testing environment for the vehicle. A physical test, which uses the standard, is the 30,000 mile endurance test. More recently, another metric has been used, the power spectral density (PSD) of road roughness. The international standard for road roughness is known as the International Roughness Index (IRI), and all road construction projects in the U.S. are based on this, as well as Department of Transportation analyses. This paper will analyze the different standards by comparing and contrasting the various aspects of each. Depending on the standard and metrics chosen, the simulation results will have different correlations with actual test.

Accurate terrain models provide the chassis designer with a powerful tool to make informed design decisions early in the design process. It is beneficial to characterize the terrain as a stochastic process, allowing limitless amounts of synthetic terrain to be created from a small number of parameters. A continuous-state Markov chain is proposed as an alternative to the traditional discrete-state chain currently used in terrain modeling practice. For discrete-state chains, the profile transitions are quantized then characterized by a transition matrix (with many values). In contrast, the transition function of a continuous-state chain represents the probability density of transitioning between any two states in the continuum of terrain heights. The transition function developed in this work uses a location-scale distribution with polynomials modeling the parameters as functions of the current state.

Hybrid powertrains derive fuel consumption benefits from using an electric motor. These benefits are more significant in city traffic than on the highway and depend on the vehicle and the driving style. Further detailed research on the fuel consumption of hybrid powertrains during drive-off procedures is rarely found in the literature. Therefore, this study focuses on analyzing the potential of a mild-hybrid powertrain, in which the electric motor is integrated with the transmission (P2.5 concept). The fuel consumption and thermal load in the drive-off element, a wet frictional clutch, are analyzed for a city cycle with a focus on the first drive-off procedure for different driving styles. Particular attention is paid to the influence of different driving styles on the torque demands of the electric motor. These simulations are realized with a so-called backward-forward model. The backward-facing part enables following a given driving cycle without considering a driver model.

Developing an energy-efficient powertrain system is a solution for environment-friendly vehicles. Furthermore, it also enhances the performance of vehicles. In powertrain system, transmission plays an important role in terms of vehicle dynamic performance and energy consumption. Therefore, a lot of researches have been conducted on modelling power losses inside the transmission. Basically, the power losses in transmission consist of bearing losses, drag torque losses on gear blank that is immersed in the oil and gear mesh losses due to the sliding frictional force on gear flank. According to some experiments in the latest literatures, power losses of synchronizers cannot be neglected, when its shift sleeve is in neutral position. Principally, power losses of synchronizers in neutral position mainly come from load independent drag torque.

In this investigation two different nonlinear dynamic black box modelling approaches are compared. The purpose of the models is to reproduce the transient gearshift process. The models are used to compute the torque at the sideshafts, which is highly correlated to the gearshift comfort. The first model is a Gaussian process (GP) model. The GP is a probabilistic, non-parametric approach, which is additionally capable to compute the confidence interval of the simulated output signal. The second black box model uses the artificial neural net (ANN) approach. In addition to training algorithms the resulting model configurations for both black box approaches are shown in this investigation. Furthermore the empirical error of both modeling approaches is compared to the predictive variance of the GP model and to the intrinsic uncertainty of the gearshift process.

As the transmission design directly impacts drive unite operation and power flow to the driveline, the transmission power loss is a critical target in the drivetrain development. The demand of more precise and more efficient power loss prediction has therefore increased significantly, which highlights the need of new methodologies in order to optimize the power loss model for vehicle transmissions. The possible power losses that exist in the power flow path, are gear mesh losses, gear churning losses, gear windage losses, bearing losses, synchronizer losses and sealing losses. Thanks to the decades of research, analytical models are available for the prediction of these component losses, which could deliver power loss distributions and overall efficiency maps of complex transmissions. The aim of this paper is to introduce a methodology to improve the accuracy of a chosen power loss model on a system level.

In this investigation an innovative signal generator will be introduced, which enables the generation of transient control signals for the gearshift process. The signals are generated merely depending on scalar transmission control unit (TCU) calibration parameters. The signal generator replaces the comprehensive TCU software within the simulation environment. Thus no extensive residual bus simulation is required. Multiple experimental models represent the core part of the signal generator. To predict the system behavior of the underlying system, the models are trained using measured data from a powertrain with automatic transmission mounted on a test rig. The results demonstrate that the introduced signal generator is suitable to predict transient control signals for the gearshift operation accurately. In combination with an additional powertrain model it is possible to simulate the gearshift process and subsequently to evaluate the gearshift comfort.

The underlying basic model represents a powertrain with automatic transmission including a torque converter. It is based on a greybox-modeling approach, which refers to ordinary differential equations with identified parameters and characteristic curves. The validated basic model is extended in order to reproduce the system behavior and especially the side shaft torque during a gear shift process. Therefore the model is extended by a transmission model with clutches for gear shifting in order to simulate specific powertrain dynamics additionally. The parameters have already been determined for the basic model using a method for isolated and structured parameter identification based on measurement data of an automotive powertrain test bench. A comparable structured parameter identification method is applied to obtain the parameters of the extended model.